Zachary R. McCaw
Updated: 2022-08-20
This package performs inference on the area under the repeated measures curve.
devtools::install_github(repo = 'zrmacc/AURMC')The function GenData simulates example data in the format expected by
this package.
library(AURMC)
set.seed(101)
data <- AURMC::GenData(
censoring_rate = 0.5,
death_rate = 0.5,
n = 25,
tau = 4
)
head(data)## idx time status value
## 1 1 0.0000000 1 0.5524619
## 2 1 1.0000000 1 -0.6749438
## 3 1 2.0000000 1 0.2143595
## 4 1 2.3638814 0 0.3107692
## 5 2 0.0000000 1 0.9170283
## 6 2 0.6309311 2 -0.2232594
The columns are:
idx, the subject identifier.time, the observation time.status, coded 0 for censoring, 1 for a measurement, and 2 for a terminal event (e.g. death).value, the value of the measurement at the observation time.
Note that:
- All subjects must have a baseline record at
time = 0withstatus = 1. - All subjects must have an observation-terminating event as their
last record, either
status = 0for censoring orstatus = 2for a terminal event.- By default, a subject with no observation-terminating event is censored immediately after their last measurement.
- If a subject’s measurement value is missing (
NA), their last value is carried forward.- If a subject’s baseline measurement is missing, it is set to zero.
The function InterpolateR provides the ability to linearly interpolate
measurements to obtain more precise area estimates:
# Simple example data.
data <- data.frame(
idx = c(1, 1, 2, 2),
status = c(1, 0, 1, 2),
time = c(0, 1, 0, 1),
value = c(0, 1, 0, -1)
)
show(data)## idx status time value
## 1 1 1 0 0
## 2 1 0 1 1
## 3 2 1 0 0
## 4 2 2 1 -1
grid <- c(0, 0.5, 1.0)
interpolated <- AURMC::Interpolate(
data = data,
grid = grid
)
show(interpolated)## idx status time value
## 1 1 1 0.0 0.0
## 2 1 1 0.5 0.5
## 3 1 0 1.0 1.0
## 4 2 1 0.0 0.0
## 5 2 1 0.5 -0.5
## 6 2 2 1.0 -1.0
Case where the expected area is zero:
set.seed(101)
data <- AURMC::GenData(
censoring_rate = 0.5,
death_rate = 0.5,
n = 25,
tau = 4
)
AURMC::AURMC(data, tau = 2.0)## method tau auc se lower upper p
## 1 asymptotic 2 -0.01260658 0.2577937 -0.517873 0.4926599 0.9609975
Case where the expected area is non-zero:
set.seed(101)
data <- AURMC::GenData(
censoring_rate = 0.5,
death_rate = 0.5,
n = 25,
tau = 4,
value_mean = 1.0
)
AURMC::AURMC(data, tau = 2.0)## method tau auc se lower upper p
## 1 asymptotic 2 1.1985 0.2735012 0.6624475 1.734553 1.1756e-05
Case of no true difference:
set.seed(102)
# Reference arm.
data0 <- AURMC::GenData(
censoring_rate = 0.5,
death_rate = 0.5,
n = 25,
tau = 4,
value_mean = 1.0
)
data0$arm <- 0
# Treatment arm.
data1 <- AURMC::GenData(
censoring_rate = 0.5,
death_rate = 0.5,
n = 25,
tau = 4,
value_mean = 1.0
)
data1$arm <- 1
data1$idx <- nrow(data0) + data1$idx
# Overall data set.
data <- rbind(data0, data1)
AURMC::CompareAURMCs(data)## Arm 0:
## method arm tau auc se lower upper p
## 1 asymptotic 0 3.6 1 0.227 0.556 1.45 1.01e-05
##
##
## Arm 1:
## method arm tau auc se lower upper p
## 1 asymptotic 1 3.6 1.34 0.278 0.793 1.88 1.47e-06
##
##
## Contrast:
## method stat est se lower upper p
## 1 asymptotic A1-A0 0.336 0.358 -0.366 1.04 0.348
## 2 asymptotic A1/A0 1.340 0.411 0.732 2.44 0.346
Case of a true differnece:
set.seed(102)
# Reference arm.
data0 <- AURMC::GenData(
censoring_rate = 0.5,
death_rate = 0.5,
n = 25,
tau = 4,
value_mean = 1.0
)
data0$arm <- 0
# Treatment arm.
data1 <- AURMC::GenData(
censoring_rate = 0.5,
death_rate = 0.5,
n = 25,
tau = 4,
value_mean = 2.0
)
data1$arm <- 1
data1$idx <- nrow(data0) + data1$idx
# Overall data set.
data <- rbind(data0, data1)
AURMC::CompareAURMCs(data)## Arm 0:
## method arm tau auc se lower upper p
## 1 asymptotic 0 3.6 1 0.227 0.556 1.45 1.01e-05
##
##
## Arm 1:
## method arm tau auc se lower upper p
## 1 asymptotic 1 3.6 2.53 0.458 1.63 3.43 3.44e-08
##
##
## Contrast:
## method stat est se lower upper p
## 1 asymptotic A1-A0 1.53 0.511 0.526 2.53 0.00281
## 2 asymptotic A1/A0 2.53 0.733 1.430 4.46 0.00140